? sci2sci – “GitHub for scientists” – AI-friendly research data management and publishing platform | Grants | Gitcoin

“At sci2sci, we are building an electronic lab notebook and a publishing platform in one interface. This will allow to store all experimental data and metadata in one place, and quickly release it in public access with one click. 

In a nutshell, we offer full stack data publishing – from the experiment planning through raw data acquisition and analysis to the final research report – all in a single platform, with a number of benefits that cannot be offered by a current journal pdf manuscript:…”

A survey of researchers’ code sharing and code reuse practices, and assessment of interactive notebook prototypes [PeerJ]

Abstract:  This research aimed to understand the needs and habits of researchers in relation to code sharing and reuse; gather feedback on prototype code notebooks created by NeuroLibre; and help determine strategies that publishers could use to increase code sharing. We surveyed 188 researchers in computational biology. Respondents were asked about how often and why they look at code, which methods of accessing code they find useful and why, what aspects of code sharing are important to them, and how satisfied they are with their ability to complete these tasks. Respondents were asked to look at a prototype code notebook and give feedback on its features. Respondents were also asked how much time they spent preparing code and if they would be willing to increase this to use a code sharing tool, such as a notebook. As a reader of research articles the most common reason (70%) for looking at code was to gain a better understanding of the article. The most commonly encountered method for code sharing–linking articles to a code repository–was also the most useful method of accessing code from the reader’s perspective. As authors, the respondents were largely satisfied with their ability to carry out tasks related to code sharing. The most important of these tasks were ensuring that the code was running in the correct environment, and sharing code with good documentation. The average researcher, according to our results, is unwilling to incur additional costs (in time, effort or expenditure) that are currently needed to use code sharing tools alongside a publication. We infer this means we need different models for funding and producing interactive or executable research outputs if they are to reach a large number of researchers. For the purpose of increasing the amount of code shared by authors, PLOS Computational Biology is, as a result, focusing on policy rather than tools.


“Introducing Reproducibility to Citation Analysis” by Samantha Teplitzky, Wynn Tranfield et al.

Abstract:  Methods: Replicated methods of a prior citation study provide an updated transparent, reproducible citation analysis protocol that can be replicated with Jupyter Notebooks.

Results: This study replicated the prior citation study’s conclusions, and also adapted the author’s methods to analyze the citation practices of Earth Scientists at four institutions. We found that 80% of the citations could be accounted for by only 7.88% of journals, a key metric to help identify a core collection of titles in this discipline. We then demonstrated programmatically that 36% of these cited references were available as open access.

Conclusions: Jupyter Notebooks are a viable platform for disseminating replicable processes for citation analysis. A completely open methodology is emerging and we consider this a step forward. Adherence to the 80/20 rule aligned with institutional research output, but citation preferences are evident. Reproducible citation analysis methods may be used to analyze open access uptake, however, results are inconclusive. It is difficult to determine whether an article was open access at the time of citation, or became open access after an embargo.

Notebook articles: towards a transformative publishing experience in nonlinear science

Abstract:  Open Science, Reproducible Research, Findable, Accessible, Interoperable and Reusable (FAIR) data principles are long term goals for scientific dissemination. However, the implementation of these principles calls for a reinspection of our means of dissemination. In our viewpoint, we discuss and advocate, in the context of nonlinear science, how a notebook article represents an essential step toward this objective by fully embracing cloud computing solutions. Notebook articles as scholar articles offer an alternative, efficient and more ethical way to disseminate research through their versatile environment. This format invites the readers to delve deeper into the reported research. Through the interactivity of the notebook articles, research results such as for instance equations and figures are reproducible even for non-expert readers. The codes and methods are available, in a transparent manner, to interested readers. The methods can be reused and adapted to answer additional questions in related topics. The codes run on cloud computing services, which provide easy access, even to low-income countries and research groups. The versatility of this environment provides the stakeholders – from the researchers to the publishers – with opportunities to disseminate the research results in innovative ways.


Ten simple rules for innovative dissemination of research

“How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society….”


Ten simple rules for innovative dissemination of research

“How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society….”


Be FAIR to your data | SpringerLink

Abstract:  Wouldn’t it be great, if experimental data were findable wherever they were? If experimental data were accessible‚ regardless of the storage place and format? If experimental data were interoperable independent of the author or its origin? If experimental data were reusable for further analysis without experimental repetition? The current state of the art of data acquisition in the laboratory is very diverse. A lot of different devices are used, analogue as well as digital ones. Usually all experimental setups and observations are summarized in a handwritten lab notebook, independently from digital or analogue sources. To change the actual and common way of laboratory data acquisition into a digital and modern one, electronic lab notebooks can be used. A challenge of science is to facilitate knowledge discovery by assisting humans and machines in their discovery of scientific data and their associated algorithms and workflows. FAIR describes a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable.


Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing | SpringerLink

Abstract: Background

A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike.

Aim of Review

To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science.

Key Scientific Concepts of Review

This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.

Breaking down the walls of scientific secrecy | CBC News

Getting scooped by a competing researcher is one of a scientist’s biggest fears. And some of the most important discoveries in medical history have been tainted by competitive controversy.

Back in 1952, before he co-discovered the structure of DNA, James Watson got access to Rosalind Franklin’s revolutionary X-ray image of DNA without her knowledge.

That image, known as Photo 51, was a major clue that helped Watson and Francis Crick complete their Nobel Prize-winning discovery. The lack of credit given to Franklin remains a stain on the story of their breakthrough.

But what if Franklin had been informally publishing her research notes all along?

“She would have gotten credit instantly for her contribution,” said Susan Lamb, a historian of medicine who holds the Hannah Chair in the History of Medicine at the University of Ottawa….”